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Iterative Semi-Global Matching for Robust Driver Assistance Systems

  • Simon Hermann
  • Reinhard Klette
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)

Abstract

Semi-global matching (SGM) is a technique of choice for dense stereo estimation in current industrial driver-assistance systems due to its real-time processing capability and its convincing performance. In this paper we introduce iSGM as a new cost integration concept for semi-global matching. In iSGM, accumulated costs are iteratively evaluated and intermediate disparity results serve as input to generate semi-global distance maps. This novel data structure supports fast analysis of spatial disparity information and allows for reliable search space reduction in consecutive cost accumulation. As a consequence horizontal costs are stabilized which improves the robustness of the matching result. We demonstrate the superiority of this iterative integration concept against a standard configuration of semi-global matching and compare our results to current state-of-the-art methods on the KITTI Vision Benchmark Suite.

Keywords

Search Space Consistency Check Stereo Match Stereo Pair Search Space Reduction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. 1.
    Ernst, I., Hirschmüller, H.: Mutual Information Based Semi-Global Stereo Matching on the GPU. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Porikli, F., Peters, J., Klosowski, J., Arns, L., Chun, Y.K., Rhyne, T.-M., Monroe, L. (eds.) ISVC 2008, Part I. LNCS, vol. 5358, pp. 228–239. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Gehrig, S.K., Eberli, F., Meyer, T.: A Real-Time Low-Power Stereo Vision Engine Using Semi-Global Matching. In: Fritz, M., Schiele, B., Piater, J.H. (eds.) ICVS 2009. LNCS, vol. 5815, pp. 134–143. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI Vision Benchmark Suite. In: Proc. Computer Vision Pattern Recognition, CVPR (2012)Google Scholar
  4. 4.
    Geiger, A., Roser, M., Urtasun, R.: Efficient Large-Scale Stereo Matching. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part I. LNCS, vol. 6492, pp. 25–38. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Gong, M., Yang, Y.-H.: Fast stereo matching using reliability-based dynamic programming and consistency constraints. In: Proc. Int. Conf. Computer Vision (ICCV), vol. 1, pp. 610–617 (2003)Google Scholar
  6. 6.
    Hermann, S., Klette, R.: Evaluation of a New Coarse-to-Fine Strategy for Fast Semi-Global Stereo Matching. In: Ho, Y.-S. (ed.) PSIVT 2011, Part I. LNCS, vol. 7087, pp. 395–406. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Hermann, S., Morales, S., Vaudrey, T., Klette, R.: Illumination Invariant Cost Functions in Semi-Global Matching. In: Koch, R., Huang, F. (eds.) ACCV Workshops 2010, Part II. LNCS, vol. 6469, pp. 245–254. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Hirschmüller, H.: Accurate and efficient stereo processing by semi-global matching and mutual information. In: Proc. IEEE Int. Conf. Computer Vision Pattern Recognition (CVPR), vol. 2, pp. 807–814 (2005)Google Scholar
  9. 9.
    Hirschmüller, H., Scharstein, D.: Evaluation of stereo matching costs on images with radiometric differences. IEEE Trans. Pattern Analysis Machine Intelligence 31, 1582–1599 (2009)CrossRefGoogle Scholar
  10. 10.
    Meister, S., Jähne, B., Kondermann, D.: Outdoor stereo camera system for the generation of real-world benchmark data sets. Optical Engineering 51, paper 021107, 6 p. (2012)Google Scholar
  11. 11.
    Ohta, Y., Kanade, T.: Stereo by two-level dynamic programming. In: Proc. Int. Joint Conf. Artificial Intelligence (IJCAI), vol. 2, pp. 1120–1126 (1985)Google Scholar
  12. 12.
    Pantillie, C., Nedevschi, S.: SORT-SGM: Subpixel optimized real-time semiglobal matching for intelligent vehicles. IEEE Trans. Vehicular Technology 61, 369–376 (2012)CrossRefGoogle Scholar
  13. 13.
    Ranftl, R., Gehrig, S., Pock, T., Bischof, H.: Pushing the limits of stereo using variational stereo estimation. In: Proc. IEEE Intelligent Vehicles Symposium (IV) (2012) (to appear)Google Scholar
  14. 14.
    Shimizu, M., Okutomi, M.: An analysis of subpixel estimation error on area-based image matching. In: Proc. IEEE Conf. Digital Signal Processing (DSP), vol. 2, pp. 1239–1242 (2002)Google Scholar
  15. 15.
    Warren, H.S.: Hacker’s Delight, pp. 65–72. Addison-Wesley Longman, New York (2002)Google Scholar
  16. 16.
    Yamaguchi, K., Hazan, T., McAllester, D., Urtasun, R.: Continuous Markov random fields for robust stereo estimation, arXiv:1204.1393v1 (2012)Google Scholar
  17. 17.
    Zach, C., Pock, T., Bischof, H.: A Duality Based Approach for Realtime TV-L 1 Optical Flow. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 214–223. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  18. 18.
    Zabih, R., Woodfill, J.: Non-Parametric Local Transforms for Computing Visual Correspondence. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 801, pp. 151–158. Springer, Heidelberg (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Simon Hermann
    • 1
  • Reinhard Klette
    • 1
  1. 1.The .enpeda.. Project, Department of Computer ScienceThe University of AucklandNew Zealand

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